deep learning - ValueError: Floating point image RGB values must be in the 0..1 range. while using matplotlib

Question

I want to visualize weights of the layer of a neural network. I'm using pytorch.

import torch
import torchvision.models as models
from matplotlib import pyplot as plt

def plot_kernels(tensor, num_cols=6):
    if not tensor.ndim==4:
        raise Exception("assumes a 4D tensor")
    if not tensor.shape[-1]==3:
        raise Exception("last dim needs to be 3 to plot")
    num_kernels = tensor.shape[0]
    num_rows = 1+ num_kernels // num_cols
    fig = plt.figure(figsize=(num_cols,num_rows))
    for i in range(tensor.shape[0]):
        ax1 = fig.add_subplot(num_rows,num_cols,i+1)
        ax1.imshow(tensor[i])
        ax1.axis('off')
        ax1.set_xticklabels([])
        ax1.set_yticklabels([])

    plt.subplots_adjust(wspace=0.1, hspace=0.1)
    plt.show()
vgg = models.vgg16(pretrained=True)
mm = vgg.double()
filters = mm.modules
body_model = [i for i in mm.children()][0]
layer1 = body_model[0]
tensor = layer1.weight.data.numpy()
plot_kernels(tensor)

The above gives this error ValueError: Floating point image RGB values must be in the 0..1 range.

My question is should I normalize and take absolute value of the weights to overcome this error or is there anyother way ? If I normalize and use absolute value I think the meaning of the graphs change.

[[[[ 0.02240197 -1.22057354 -0.55051649]
   [-0.50310904  0.00891289  0.15427093]
   [ 0.42360783 -0.23392732 -0.56789106]]

  [[ 1.12248898  0.99013627  1.6526649 ]
   [ 1.09936976  2.39608836  1.83921957]
   [ 1.64557672  1.4093554   0.76332706]]

  [[ 0.26969245 -1.2997849  -0.64577204]
   [-1.88377869 -2.0100112  -1.43068039]
   [-0.44531786 -1.67845118 -1.33723605]]]


 [[[ 0.71286005  1.45265901  0.64986968]
   [ 0.75984162  1.8061738   1.06934202]
   [-0.08650422  0.83452386 -0.04468433]]

  [[-1.36591709 -2.01630116 -1.54488969]
   [-1.46221244 -2.5365622  -1.91758668]
   [-0.88827479 -1.59151018 -1.47308767]]

  [[ 0.93600738  0.98174071  1.12213969]
   [ 1.03908169  0.83749604  1.09565806]
   [ 0.71188802  0.85773659  0.86840987]]]


 [[[-0.48592842  0.2971966   1.3365227 ]
   [ 0.47920835 -0.18186836  0.59673625]
   [-0.81358945  1.23862112  0.13635623]]

  [[-0.75361633 -1.074965    0.70477796]
   [ 1.24439156 -1.53563368 -1.03012812]
   [ 0.97597247  0.83084011 -1.81764793]]

  [[-0.80762428 -0.62829626  1.37428832]
   [ 1.01448071 -0.81775147 -0.41943246]
   [ 1.02848887  1.39178836 -1.36779451]]]


 ..., 
 [[[ 1.28134537 -0.00482408  0.71610934]
   [ 0.95264435 -0.09291686 -0.28001019]
   [ 1.34494913  0.64477581  0.96984017]]

  [[-0.34442815 -1.40002513  1.66856039]
   [-2.21281362 -3.24513769 -1.17751861]
   [-0.93520379 -1.99811196  0.72937071]]

  [[ 0.63388056 -0.17022935  2.06905985]
   [-0.7285465  -1.24722099  0.30488953]
   [ 0.24900314 -0.19559766  1.45432627]]]


 [[[-0.80684513  2.1764245  -0.73765725]
   [-1.35886598  1.71875226 -1.73327696]
   [-0.75233924  2.14700699 -0.71064663]]

  [[-0.79627383  2.21598244 -0.57396138]
   [-1.81044972  1.88310981 -1.63758397]
   [-0.6589964   2.013237   -0.48532376]]

  [[-0.3710472   1.4949851  -0.30245575]
   [-1.25448656  1.20453358 -1.29454732]
   [-0.56755757  1.30994892 -0.39370224]]]


 [[[-0.67361742 -3.69201088 -1.23768616]
   [ 3.12674141  1.70414758 -1.76272404]
   [-0.22565465  1.66484773  1.38172317]]

  [[ 0.28095332 -2.03035069  0.69989491]
   [ 1.97936332  1.76992691 -1.09842575]
   [-2.22433758  0.52577412  0.18292744]]

  [[ 0.48471382 -1.1984663   1.57565165]
   [ 1.09911084  1.31910467 -0.51982772]
   [-2.76202297 -0.47073677  0.03936549]]]]

Answer 1

It sounds as if you already know your values are not in that range. Yes, you must re-scale them to the range 0.0 - 1.0. I suggest that you want to retain visibility of negative vs positive, but that you let 0.5 be your new "neutral" point. Scale such that current 0.0 values map to 0.5, and your most extreme value (largest magnitude) scale to 0.0 (if negative) or 1.0 (if positive).

---

Thanks for the vectors. It looks like your values are in the range -2.25 to +2.0. I suggest a rescaling new = (1/(2*2.25)) * old + 0.5